Learning motion dynamics to catch a moving object
Open accessAbstract
In this paper, we consider a novel approach to control the timing of motions when these are encoded with autonomous dynamical systems (DS). Accurate timing of motion is crucial if a robot must synchronize its movement with that of a fast moving object. In previous work of ours [1], we developed an approach to encode robot motion into DS. Such a time-independent encoding is advantageous in that it offers robustness against violent perturbation by adapting on the fly the trajectory while ensuring high accuracy at the target. We propose here an extension of the system that allows to control the timing of the motion while still benefitting from all the robustness properties deriving from the time-independent encoding of the DS. We validate the approach in experiments where the iCub robot learns from human demonstrations to catch a ball on the fly.
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The dynamical systems approach to encoding motion from demonstrations enables rapid and reactive adaptation of the arm motion in the presence of sensor uncertainty.
“We use the dynamical systems approach to encode motion from several demonstrations. This enables a rapid and reactive adaptation of the arm motion in the presence of sensor uncertainty.”
From Catching Objects in Flight (2014) · cites this paper as [23]
The paper combines three strands of previous work: learning to predict trajectory of fast-moving objects [22], learning hand-finger coordinated motion [38], and controlling timing of motion with DSs [23].
“We combine three strands of our previous works on 1) learning how to predict accurately the trajectory of fast-moving objects [22], 2) learning hand-finger coordinated motion [38], and 3) controlling the timing of motion when controlled with DSs [23].”
From Catching Objects in Flight (2014) · cites this paper as [23]
Cites
Further progress in robot juggling: solvable mirror laws
Rizzi, A. A., & Koditschek, D. E. (2002) · Proceedings of the 1994 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.1994.350893 · Cited by 50 · see citing works
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Kim, S., Shukla, A., & Billard, A. (2014) · IEEE Transactions on Robotics · doi:10.1109/tro.2014.2316022 · Cited by 220 · see citing works
Self-citationLearning to Play Minigolf: A Dynamical System-Based Approach
Khansari-Zadeh, S. M., Kronander, K., & Billard, A. (2012) · Advanced Robotics · doi:10.1080/01691864.2012.728692 · Cited by 25 · see citing works
Self-citation
- Authors
- Kim, S., Gribovskaya, E., & Billard, A.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- ORCID
- Billard, Aude
- Venue
- 2010 10th IEEE-RAS International Conference on Humanoid Robots
- Publisher
- IEEE
- Published
- 2010-12
- OpenAlex
- W2146693276
- Cited by
- 39
- References
- 1
- Citations by year
- 2025: 1 · 2024: 3 · 2021: 3 · 2020: 2 · 2019: 1 · 2018: 4 · 2017: 5 · 2016: 5 · 2015: 2 · 2014: 4 · 2013: 1 · 2012: 4
- FWCI
- 5.35
- Type
- conference paper
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- IEEE-RAS International Conference on Humanoid Robots (Humanoids) (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- autonomous dynamical systems, iCub humanoid robot, human demonstration learning, trajectory adaptation, robustness to disturbances, robot synchronization
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